Systems today may be very complex and monitoring such systems may yield massive amounts of data of high dimension (e.g., hundreds, thousands or millions of rows and/or columns when data is represented in tabular format) representing numerous aspects of real world activities by users, system components, and so forth. Dealing with these data sets and extracting useful information to base decisions on is difficult, if not impossible. Many statistical learning algorithms and machine learning methodologies do not work properly for high dimension data. Thus, almost all techniques to deal with this type of high dimension data involve reduction in the number of dimensions through one approach or another until the dimensions are of an order sufficient to allow the application of the desired techniques. Most, if not all, of these techniques yield reduced dimensions that are not tied to the real world (e.g., the ability to interpret the resulting reduced dimensions in terms of real world activities by users, system components, and so forth is lost).